Probing the Limit of Heat Transfer in Inorganic Crystals with Deep Learning - Li, Jielan; Chen, Zekun; Wang, Qian; Yang, Han; Lu, Ziheng; Li, Guanzhi; Chen, Shuizhou; Zhu, Yu; Liu, Xixian; Tan, Junfu; Tang, Mingfa; Zhou, Yichi; et al. (2025)
Heat transfer is a fundamental property of matter. Research spanning decades has attempted to discover materials with exceptional thermal conductivity, yet the upper limit remains unknown. Using deep learning accelerated crystal structure prediction and first-principles calculation, we systematically explore the thermal conductivity landscape of inorganic crystals. We brute-force over half a million ordered crystalline structures, encompassing an extensive coverage of local energy minima in binary compounds with up to four atoms per primitive cell. We confirm diamond sets the upper bound of thermal conductivity within our search space, very likely also among all stable crystalline solids at ambient conditions. We identify over 20 novel crystals with high thermal conductivity surpassing silicon at room temperature validated by density functional theory. These include a series of metallic compounds, especially MnV, exhibiting high lattice and electronic thermal conductivity simultaneously, a distinctive feature not observed before. The fast deep learning-driven screening method, as well as the large comprehensive thermal conductivity database, pave the way for the discovery and design of next-generation materials with tailored thermal properties.